Objective To explore the feasibility and reference value of allogeneic lung transplantation and postoperative monitoring in miniature pigs for lung transplantation research. Methods Two miniature pigs (R1 and R2) underwent left lung allogeneic transplantation. Complement-dependent cytotoxicity tests and blood cross-matching were performed before surgery. The main operative times and partial pressure of arterial oxygen (PaO₂) after opening the pulmonary artery were recorded during surgery. Postoperatively, routine blood tests, biochemical blood indicators and inflammatory factors were detected, and pathological examinations of multiple organs were conducted. Results The complement-dependent cytotoxicity test showed that the survival rate of lymphocytes between donors and recipients was 42.5%-47.3%, and no agglutination reaction occurred in the cross-matching. The first warm ischemia times of D1 and D2 were 17 min and 10 min, respectively, and the cold ischemia times were 246 min and 216 min, respectively. Ultimately, R1 and R2 survived for 1.5 h and 104 h, respectively. Postoperatively, in R1, albumin (ALB) and globulin (GLB) decreased, and alanine aminotransferase increased; in R2, ALB, GLB and aspartate aminotransferase all increased. Urea nitrogen and serum creatinine increased in both recipients. Pathological results showed that in R1, the transplanted lung had partial consolidation with inflammatory cell infiltration, and multiple organs were congested and damaged. In R2, the transplanted lung had severe necrosis with fibrosis, and multiple organs had mild to moderate damage. The expression levels of interleukin-1β and interleukin-6 increased in the transplanted lungs. Conclusions The allogeneic lung transplantation model in miniature pigs may systematically evaluate immunological compatibility, intraoperative function and postoperative organ damage. The data obtained may provide technical references for subsequent lung transplantation research.

With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveBased on ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， the chemical constituents of Qili Qiangxin capsules was identified， and their distribution in vivo was analyzed. MethodsUPLC-Q-Orbitrap-MS was used to detect the sample solution of Qili Qiangxin capsules， as well as the serum， brain， heart， lung， spleen， liver and kidney tissues of mice after oral administration. Using the Thermo Xcalibur 2.2 software， the compound information database was constructed， and the molecular formulas of compounds corresponding to the quasi-molecular ions were fitted. Based on the information of retention time， accurate relative molecular mass and fragments， the compounds and their distribution in vivo were analyzed by comparing with the data of reference substances and literature. ResultsA total of 233 compounds， including 70 terpenoids， 60 flavonoids， 23 organic acids， 17 alkaloids， 20 steroids， 7 coumarins and 36 others， were identified or predicted from Qili Qiangxin capsules， 73 of which were identified matching with standard substances. Tissue distribution results showed that 71， 17， 38， 33， 32， 58 and 43 migrating components were detected in blood， brain， heart， lung， spleen， liver and kidney， respectively. Thirty-seven components were absorbed into the blood and heart， including quinic acid， benzoylaconitine benzoylmesaconine and so on. Fourteen components were absorbed into the blood and six tissues， including calycosin， methylnissolin， formononetin， alisol B， alisol A and so on. ConclusionThis study comprehensively analyzes the chemical components of Qili Qiangxin capsules and their distribution in vivo. Among them， astragaloside Ⅳ， salvianolic acid B， ginsenoside Rb₁， ginsenoside Rb₃， ginsenoside Rd， ginsenoside Rg₃， calycosin-7-glucoside， and sinapine may be the important components for the treatment of heart failure， which can provide useful reference for its quality control and research on pharmacodynamic material basis.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

Objective:To evaluate the efficacy and safety of the pegaspargase, etoposide, methotrexate, and dexamethasone (PEMD) regimen in patients with early-stage nonupper respiratory digestive tract or advanced extranodal natural killer/T-cell lymphoma (ENKTL) .Methods:This retrospective analysis included 38 patients with newly diagnosed early-stage non-upper respiratory digestive tract or advanced ENKTL who received PEMD regimen for induction chemotherapy at the First Affiliated Hospital of Nanjing Medical University from January 2016 to December 2022. Survival outcomes and prognostic factors were examined by Kaplan-Meier, and the Log-rank test was used to compare survival.Results:The study population had a median age of 48 years (range, 26-72 years) and included 30 males (78.9%) and 8 females (21.1%). 7 patients’ age >60 years (18.4%). The Eastern Cooperative Oncology Group (ECOG) performance score was >1 in 7 patients (18.4%) ; 20 patients (52.6%) had elevated lactate dehydrogenase levels; and 37 patients (97.4%) exhibited extranodal involvement. Using the Ann Arbor staging system, 37 patients (97.4%) were classified as stage Ⅲ-Ⅳ. The median number of treatment cycles was 5 (1-6), and the median follow-up duration was 60 months (24 - 101 months). Interim efficacy assessment revealed an overall response rate of 52.7%. At 2 and 4 years, the progression-free survival (PFS) rates were 34.2% (95% CI 22.0%-53.2%) and 25.5% (95% CI 14.7%-44.4%), respectively, and the overall survival rates were 50.0% (95% CI 36.4%-68.7%) and 45.5% (95% CI 31.4%-65.7%), respectively. The risk factors for worse PFS were ECOG performance score >1 [ HR=3.711 (95% CI 1.494-9.218), P=0.005]; bone marrow infiltration [ HR=2.251 (95% CI 1.026 - 4.938), P=0.043]; and Prognostic Index for Natural Killer/T-Cell Lymphoma score of 3 - 5 [ HR=2.350 (95% CI 1.009 - 5.476), P=0.048]. Multivariate analysis identified ECOG performance score >1 as an independent risk factor for PFS [ HR=7.971 (95% CI 2.222 - 28.591), P=0.001]. The main adverse effect was anemia in 31 patients (81.6%) . Conclusion:The PEMD regimen was safe and effective for patients with newly diagnosed early-stage non-upper respiratory digestive tract or advanced ENKTL.

Objective:To analyze the correlation between mental health status and metabolic syndrome (MetS) in health checkup people.Methods:It was a cross-sectional study, 2 920 participants who received health checkup in the Health Examination Center of Peking University Third Hospital from January 2019 to December 2023 were selected using cluster sampling method. Their general information, physical examination, biochemical indicators, body composition, and self-evaluation scores on the Symptom Checklist-90 (SCL-90) were collected. According to the inclusion and exclusion criteria, a total of 2 813 study subjects were included, and divided into the MetS group and the non-MetS group based on whether they had MetS. The differences in general demographic information, body composition, blood biochemistry, and SCL-90 scores between the two groups were compared. Binary Logistic regression analysis was used to explore the correlation between mental health status and MetS.Results:Of the 2 813 subjects included, 1 576 were males (56.0%) and 1 237 were females (44.0%), with an average age of (41.7±11.0) years, the MetS group had 586 cases (20.8%) and the non-MetS group had 2 227 cases (79.2%). The MetS group had higher levels of age, body mass index, waist circumference, systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides, low density lipoprotein cholesterol (LDL-C), fasting blood glucose, glycosylated hemoglobin (HbA 1c), free thyroxine(FT4), total triiodothyronine (TT3), free triiodothyronine (FT3), waist-to-hip ratio, visceral fat area, body fat percentage, uric acid/creatinine, homocysteine (Hcy), aspartate aminotransferases (AST), and alanine transaminase (ALT) levels, as well as higher scores for somatization, hostility, paranoia, and other factor compared to the non-MetS group (all P<0.05), while high density lipoprotein cholesterol (HDL-C) and estimated glomerular filtration rate (eGFR) levels were lower than those in the non-MetS group (all P<0.05). The proportion of male, and the positive rates of SCL-90, somatization, interpersonal sensitivity, hostility, paranoia and other factor in the MetS group were higher than those in the non-MetS group (all P<0.05). Multifactorial analysis showed that individuals with a positive SCL-90 assessment had a 1.34 times higher risk of MetS than those with a negative assessment ( OR=1.34, 95% CI: 1.06-1.68; P=0.014). Among them, individuals with positive somatization ( OR=2.02, 95% CI: 1.25-3.28; P=0.004) and hostility ( OR=1.61, 95% CI: 1.02-2.56; P=0.042) had increased risk of MetS. Conclusion:Poor mental health status increases the risk of MetS.